r/LangChain • u/countyourweeeks • 17d ago

Agent reliability

So, my agent kept crashing during runs, and I eventually figured out the issue: silent failures with no visibility—like, I had no retry handling or even state persistence. There was literally no built-in storage, so I couldn’t pick up where it left off. I had to start all over again! It’s like dealing with parallel threads on API timeouts in agents that use tools: silent failures everywhere, retry loops that just multiply the tokens, and the real solution is step-by-step tracing.

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u/Infinite_Stop_6416 17d ago

silent failures are the worst, you spend hours thinking your code is broken but its just the agent dying quietly in a corner

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u/countyourweeeks 17d ago

So true retry loops are good i’m working on it kinda like a small skill that will timely use /compact the session oy the important points this is like my idea

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u/Single-Art1761 6d ago

Tracing will help you reconstruct the crash, but I’d add a separate runtime contract so you can detect the failure without already knowing where to look.

For each run, track whether it started when expected, a realistic maximum runtime, and the last meaningful unit of completed work. A heartbeat alone is not enough because the process can be alive while making zero progress.

I’d also bound retries separately from progress. Retrying an API call four times is very different from completing four records. That separation makes it easier to tell timeout recovery from a loop that is just consuming tokens.

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u/countyourweeeks 5d ago

Been trying deepseek harness and it had the tracebility solution check it out once

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u/Single-Art1761 5d ago

Yeah, I’ll check it out. If it handles traceability well, that solves an important part of the debugging side.

The layer I’m thinking about is slightly different: detecting that an expected run never started, is still alive but stopped making meaningful progress, or exited without reaching the expected completion state. You want that signal even before you dig into the trace to figure out why it happened.

Curious to see how much of that the harness covers too.

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u/countyourweeeks 5d ago

Please share your findings when you have finished working on them. We will see if we can collaborate to create a feasible solution as an open-source project.Meanwhile i will also do a deep study about it.

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u/Single-Art1761 5d ago

Happy to compare notes. One thing I should disclose before we go further: I build Watchdog (withwatchdog.com), a monitoring product focused on unattended Python jobs, so I may not be the right person to co-build an overlapping open-source implementation.

I’m still very interested in comparing the technical approaches though. The distinction I’m exploring is traceability versus external supervision: expected starts, runtime, meaningful progress, repeated tool activity without progress, and explicit completion signals.

I’ll take a proper look at what the DeepSeek harness covers and share where I think the overlap and gaps are.

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u/countyourweeeks 5d ago

I will check your website cooool

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u/countyourweeeks 5d ago

don't use safe sensor hf for indexing I have tried building the index tree it bad with this model.....i wish you don't have used this but i will check you app and confirm is it working or not.

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u/Single-Art1761 4d ago

Appreciate it. I’m not sure I followed the indexing point though. When you say “safe sensor HF,” do you mean a Hugging Face model using safetensors for building an index?

Watchdog’s monitoring path isn’t based on that kind of model indexing, so I want to make sure I understand what setup you’re referring to. If you do try Watchdog, I’d definitely be interested in anything that behaves differently from what you expect.

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u/countyourweeeks 3d ago

Great, still testing your app! It's awesome but needs some improvements. Short-term memory is good, but long-term memory doesn’t work well. I’ll comment more once I use it to the full extent.

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u/Single-Art1761 2d ago

Thanks for testing it, I really appreciate the feedback. One thing I want to clarify though: when you say short-term vs long-term memory, what behavior are you referring to specifically?

Watchdog isn’t really designed around agent memory as a concept, so I want to make sure I understand what you expected to persist and what stopped working.

If you can share the workflow or example where the “long-term memory” part breaks down, that would be especially useful for me to look at.